NotebookLM Export Ingestion And Archive

Overview

This document defines the intake workflow for three newly downloaded NotebookLM export bundles currently stored under .temp/.

The user requested a deep analysis of the downloaded files, plus a safe renaming and relocation plan that does not interfere with the active training campaign running on another machine.

The three current export bundles are:

• Neural Network Foundations

• TE Model Curriculum - From Baselines to Physics-Informed Neural Networks

• Multilayer Perceptrons - Foundations of Neural Network Architecture

Two of these themes already correspond to repository-owned learning guides:

• doc/reports/analysis/learning_guides/Neural Network Foundations/

• doc/reports/analysis/learning_guides/TE Model Curriculum/

The third bundle is conceptually adjacent to the existing Wave 1 architecture guide family, but it is not a one-to-one match for a current canonical folder.

Because the active campaign is ongoing and the user explicitly requested standalone work only, the imported NotebookLM artifacts should be archived as standalone external outputs rather than merged directly into the canonical guide folders at this stage.

Technical Approach

Current File Inventory

The downloaded bundles contain the following file classes:

Neural Network Foundations

• long-form presentation PDF: Neural_Network_Foundations.pdf

• presentation deck: Neural_Network_Foundations.pptx

• video overview: Reti_Neurali_e_Previsione_TE.mp4

• mind-map image: Mind Map.png

• additional generated image: unnamed.png

• short supporting PDF: Dalla Regressione Lineare ai Neuroni Artificiali_ Un Viaggio nella Predizione dell'Errore di Trasmissione.pdf

TE Model Curriculum

• long-form presentation PDF: Transmission_Error_Model_Evolution.pdf

• presentation deck: Transmission_Error_Model_Evolution.pptx

• video overview: Il_Curriculum_dei_Modelli_TE.mp4

• mind-map image: Mind Map.png

• additional generated image: unnamed.png

• short supporting PDF: Panoramica delle Architetture Neurali_ Guida al Curriculum TE.pdf

Multilayer Perceptrons

• long-form presentation PDF: Harmonic_Neural_Synthesis.pdf

• presentation deck: Harmonic_Neural_Synthesis.pptx

• video overview: Modellare_Pattern_Periodici.mp4

• mind-map image: Mind Map.png

• additional generated image: Evoluzione dell'Analisi Armonica.png

• short supporting PDF: Oltre la Linea_ Come le Reti Neurali Risolvono l'Enigma dello XOR.pdf

Why These Files Should Not Be Merged Directly Into Canonical Guide Folders

The repository already distinguishes between:

• canonical repository-authored learning guides;

• report-local generated assets;

• NotebookLM video-guide source packages;

• temporary or imported artifacts.

The downloaded files are not repository-authored guide sources.

They are generated external outputs from NotebookLM and should remain identifiable as such.

If they are dropped directly into the existing learning-guide folders, the repository risks mixing:

• canonical Markdown and approved PDF deliverables;

• externally generated presentation decks;

• externally generated video exports;

• raw generated image artifacts with inconsistent names.

That would blur the boundary between authoritative repository documentation and imported supporting media.

Recommended Standalone Archive Layout

The correct standalone location is a dedicated import archive:

• doc/imports/notebooklm_exports/

Each bundle should then be stored under a normalized topic folder:

• doc/imports/notebooklm_exports/neural_network_foundations/

• doc/imports/notebooklm_exports/te_model_curriculum/

• doc/imports/notebooklm_exports/multilayer_perceptrons/

Inside each topic folder, the artifacts should be split by type:

• pdf/

• slides/

• video/

• images/

This preserves:

• standalone isolation during the active campaign;

• clean provenance for imported assets;

• later selective integration into canonical guide folders after the campaign ends.

Recommended File Renaming Policy

All imported files should receive:

• English-first normalized filenames;

• explicit topic prefixes;

• artifact-type clarity;

• no generic names such as Mind Map.png or unnamed.png.

Proposed rename map:

Neural Network Foundations

• Neural_Network_Foundations.pdf -> neural_network_foundations_notebooklm_slides.pdf

• Neural_Network_Foundations.pptx -> neural_network_foundations_notebooklm_slides.pptx

• Reti_Neurali_e_Previsione_TE.mp4 -> neural_network_foundations_notebooklm_video_overview.mp4

• Mind Map.png -> neural_network_foundations_notebooklm_mind_map.png

• unnamed.png -> neural_network_foundations_notebooklm_supporting_figure.png

• Dalla Regressione Lineare ai Neuroni Artificiali_ Un Viaggio nella Predizione dell'Errore di Trasmissione.pdf -> neural_network_foundations_notebooklm_supporting_brief.pdf

TE Model Curriculum

• Transmission_Error_Model_Evolution.pdf -> te_model_curriculum_notebooklm_slides.pdf

• Transmission_Error_Model_Evolution.pptx -> te_model_curriculum_notebooklm_slides.pptx

• Il_Curriculum_dei_Modelli_TE.mp4 -> te_model_curriculum_notebooklm_video_overview.mp4

• Mind Map.png -> te_model_curriculum_notebooklm_mind_map.png

• unnamed.png -> te_model_curriculum_notebooklm_supporting_figure.png

• Panoramica delle Architetture Neurali_ Guida al Curriculum TE.pdf -> te_model_curriculum_notebooklm_supporting_brief.pdf

Multilayer Perceptrons

• Harmonic_Neural_Synthesis.pdf -> multilayer_perceptrons_notebooklm_slides.pdf

• Harmonic_Neural_Synthesis.pptx -> multilayer_perceptrons_notebooklm_slides.pptx

• Modellare_Pattern_Periodici.mp4 -> multilayer_perceptrons_notebooklm_video_overview.mp4

• Mind Map.png -> multilayer_perceptrons_notebooklm_mind_map.png

• Evoluzione dell'Analisi Armonica.png -> multilayer_perceptrons_notebooklm_supporting_figure.png

• Oltre la Linea_ Come le Reti Neurali Risolvono l'Enigma dello XOR.pdf -> multilayer_perceptrons_notebooklm_supporting_brief.pdf

Post-Campaign Integration Path

After the active campaign ends, the archive can be reviewed for selective integration.

The likely future mapping is:

• neural_network_foundations archive -> selectively referenced from doc/reports/analysis/learning_guides/Neural Network Foundations/

• te_model_curriculum archive -> selectively referenced from doc/reports/analysis/learning_guides/TE Model Curriculum/

• multilayer_perceptrons archive -> either promoted to a new learning-guide family or split into Wave 1 model-family supporting media

Until that later review, the archive should remain standalone and should not overwrite any existing canonical guide asset.

Involved Components

• .temp/ Current staging location of the downloaded NotebookLM export bundles.

• doc/imports/notebooklm_exports/ Proposed standalone destination root for imported external NotebookLM outputs.

• doc/reports/analysis/learning_guides/Neural Network Foundations/ Existing canonical guide related to one imported bundle.

• doc/reports/analysis/learning_guides/TE Model Curriculum/ Existing canonical guide related to one imported bundle.

• doc/reports/analysis/learning_guides/ Parent location for future post-campaign selective integration decisions.

• README.md Main project document that must reference this technical planning note.

• doc/README.md Internal documentation index that should also reference this technical planning note.

Implementation Steps

1. Create this technical planning document and register it in README.md and doc/README.md.

2. Wait for explicit user approval before renaming or moving any downloaded file.

3. After approval, create doc/imports/notebooklm_exports/ and the three normalized topic folders.

4. Move the downloaded artifacts out of .temp/ into the archive folders and apply the normalized filenames listed in this document.

5. Produce a final inventory report that maps original paths to archived paths.

6. Leave canonical learning-guide folders unchanged until the active campaign ends and the user explicitly requests integration.